Aligning Natural Language to Linking Code Snippets to Perform a Complicated Task

ABSTRACT

A method, system and computer-usable medium for linking a set of executable code snippets to perform a complicated task, comprising: decomposing a natural language statement into a plurality of decomposed natural language components; searching a repository of code snippets to identify code snippets corresponding to each of the decomposed natural language components; ordering execution of the code snippets based upon the plurality of decomposed natural language components; and, executing the code snippets in order of the natural language statement requests until a final outcome is achieved.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for analyzing and deducing criteria-relatedcontent for evaluation.

Description of the Related Art

Natural language processing (NLP) refers to the technology that allowscomputers to understand, or derive meaning from, human languages, be itspoken or written. In general, NLP systems determine meaning from text.The meaning, and potentially other information extracted from the text,can be provided to other systems. For example, an NLP system used for anairline can be trained to recognize user intentions such as making areservation, canceling a reservation, checking the status of a flight,etc. from received text. The text provided to the NLP system as inputcan be obtained from a speech recognition system, keyboard entry, orsome other mechanism. The NLP system determines the meaning of the textand typically provides the meaning, or user intention, to one or moreother applications. The meaning can drive business logic, effectivelytriggering some programmatic function corresponding to the meaning Forexample, responsive to a particular meaning, the business logic caninitiate a function such as creating a reservation, canceling areservation, etc.

One issue relating to NLP is when the text is associated with arelatively complicated task (i.e., a task which includes a plurality ofdiscrete sub-tasks). For example, a user may wish to perform acomplicated task, but not know how to write the software code to performthese tasks. In such a situation it would be desirable to provide an NLPsystem which, based upon a predetermined goal and a received set oftext, can link a set of executable code snippets that are deducted fromthe natural language of the text to perform a more complicated task.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed for linking aset of executable code snippets that are deducted from natural languagetext sources, to perform a complicated task. For the purposes of thisdisclosure, a code snippet comprises executable code that executes on aprocessor and performs at least one discrete task. A code snippet may beexecuted in series or in parallel with other executable code. The set ofcode snippets are analyzed based on a predetermined goal.

More specifically, in certain embodiments, the invention includes acomplex task analysis operation which analyzes content such as textutilizing natural language processing (NLP) to identify programmabletasks, identify code matching the programmable tasks and execute thecode. For the purposes of this disclosure, a complex task comprises adesired task that can be performed using a computer executable algorithmwhere the computer executable algorithm is comprised of a plurality ofcode snippets. In various embodiments, the complex task analysisoperation includes applying natural language processing (NLP) to content[e.g., a document or other content source] to form a series ofoperational descriptions D1, D2, . . . Dn, wherein for each Di there isan input Ii, an output Oi, and an operational description Di mapping theIi to the Oi; searching a repository for a code segment (Ci)[implementation] of each Di to form an executable Ei; converting theseries of Di into the series of Ei; aligning and converting the outputOi to subsequent snippet input Ii+1 of executable Ei+1; and executingthe Ei in sequential order (E1, E2, . . . En) to implement the series ofoperational descriptions in the content. Additionally, in variousembodiments, the complex task analysis operation includes mapping eachDi to a programming construct comprising terms, data types, and verbs;matching the data types to a programming language to determineparameters; and applying a similarity algorithm to identify the codesegment Ci in the repository. Additionally, in various embodiments, thecomplex task analysis operation includes providing an artificialintelligence (AI) [e.g., machine language (ML)] component with userassistance to the similarity operation.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented.

FIG. 2 is a simplified block diagram of an information handling systemcapable of performing computing operations.

FIG. 3 is a generalized depiction of a complex task analysis operation.

FIG. 4 shows a block diagram of a complex task analysis system.

FIG. 5 shows a generalized flowchart of the complex task analysisoperation.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for performinga complex task analysis operation.

In certain embodiments, the complex task analysis operation receives aset of natural language statements, breaks the statements apart intosimple steps, and matches these steps to a related set of correlatableexecutable code snippets, and then executes the set of code snippets inthe correct order to accomplish the goal of the natural languagestatement. Additionally, the natural language statements are correlatedwith the functionality of the code snippets, the input(s) and output(s)of the snippets, and the ordering of the snippets by way of matching thecode snippets to the subsequent snippets to execute a chain of snippetsto reach a final outcome.

The complex task analysis operation identifies terms that map tooperations or sets of operations for a code snippet, and terms thatdescribe types of input (parameters) for methods in a programminglanguage snippet. Additionally, the complex task analysis operationorders the execution of valid snippets that match the type and createsan on-demand parameters list from both NLP statements and output of aprevious snippet. Additionally, the complex task analysis operationmatches and alters the configuration for input/output for any of aplurality of programming language types.

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion prioritization system 10 and Question Answering (QA) system 100connected to a computer network 140. The QA system 100 includes aknowledge manager 104 that is connected to a knowledge base 106 andconfigured to provide Question Answering (QA) generation functionalityfor one or more content users who submit questions across the network140 to the QA system 100. To assist with efficient sorting andpresentation of questions to the QA system 100, the prioritizationsystem 10 may be connected to the computer network 140 to receive userquestions, and may include a plurality of subsystems which interact withcognitive systems, like the knowledge manager 100, to prioritizequestions or requests being submitted to the knowledge manager 100.

The Named Entity subsystem 12 receives and processes each question 11 byusing Natural Language (NL) processing to analyze each question andextract question topic information contained in the question, such asnamed entities, phrases, urgent terms, and/or other specified termswhich are stored in one or more domain entity dictionaries 13. Byleveraging a plurality of pluggable domain dictionaries relating todifferent domains or areas (e.g., travel, healthcare, electronics, gameshows, financial services), the domain dictionary 11 enables criticaland urgent words (e.g., “account balance”) from different domains (e.g.,“banking”) to be identified in each question based on their presence inthe domain dictionary 11. To this end, the Named Entity subsystem 12 mayuse a Natural Language Processing (NLP) routine to identify the questiontopic information in each question. As used herein, “NLP” refers to thefield of computer science, artificial intelligence, and linguisticsconcerned with the interactions between computers and human (natural)languages. In this context, NLP is related to the area of human-computerinteraction and natural language understanding by computer systems thatenable computer systems to derive meaning from human or natural languageinput. For example, NLP can be used to derive meaning from ahuman-oriented question such as, “How can I calculate my bank balance?”and to identify specified terms, such as named entities, phrases, orurgent terms contained in the question. The process identifies key termsand attributes in the question and compares the identified terms to thestored terms in the domain dictionary 13.

The Question Priority Manager subsystem 14 performs additionalprocessing on each question to extract question context information 15A.In addition or in the alternative, the Question Priority Managersubsystem 14 may also extract server performance information 15B for thequestion prioritization system 10 and/or QA system 100. In selectedembodiments, the extracted question context information 15A may includedata that identifies the user context and location when the question wassubmitted or received. For example, the extracted question contextinformation 15A may include data that identifies the user who submittedthe question (e.g., through login credentials), the device or computerwhich sent the question, the channel over which the question wassubmitted, the location of the user or device that sent the question,any special interest location indicator (e.g., hospital, public-safetyanswering point, etc.), or other context-related data for the question.The Question Priority Manager subsystem 14 may also determine or extractselected server performance data 15B for the processing of eachquestion. In selected embodiments, the server performance information15B may include operational metric data relating to the availableprocessing resources at the question prioritization system 10 and/or QAsystem 100, such as operational or run-time data, CPU utilization data,available disk space data, bandwidth utilization data, etc. As part ofthe extracted information 15A/B, the Question Priority Manager subsystem14 may identify the SLA or QoS processing requirements that apply to thequestion being analyzed, the history of analysis and feedback for thequestion or submitting user, and the like. Using the question topicinformation and extracted question context and/or server performanceinformation, the Question Priority Manager subsystem 14 is configured topopulate feature values for the Priority Assignment Model 16 whichprovides a machine learning predictive model for generating a targetpriority values for the question, such as by using an artificialintelligence (AI) rule-based logic to determine and assign a questionurgency value to each question for purposes of prioritizing the responseprocessing of each question by the QA system 100.

The Prioritization Manager subsystem 17 performs additional sort or rankprocessing to organize the received questions based on at least theassociated target priority values such that high priority questions areput to the front of a prioritized question queue 18 for output asprioritized questions 19. In the question queue 18 of the PrioritizationManager subsystem 17, the highest priority question is placed at thefront for delivery to the assigned QA system 100. In selectedembodiments, the prioritized questions 19 from the PrioritizationManager subsystem 17 that have a specified target priority value may beassigned to a specific pipeline (e.g., QA System 100A) in the QA systemcluster 100. As will be appreciated, the Prioritization Managersubsystem 17 may use the question queue 18 as a message queue to providean asynchronous communications protocol for delivering prioritizedquestions 19 to the QA system 100 such that the Prioritization Managersubsystem 17 and QA system 100 do not need to interact with a questionqueue 18 at the same time by storing prioritized questions in thequestion queue 18 until the QA system 100 retrieves them. In this way, awider asynchronous network supports the passing of prioritized questionsas messages between different computer systems 100A, 100B, connectingmultiple applications and multiple operating systems. Messages can alsobe passed from queue to queue in order for a message to reach theultimate desired recipient. An example of a commercial implementation ofsuch messaging software is IBM's WebSphere MQ (previously MQ Series). Inselected embodiments, the organizational function of the PrioritizationManager subsystem 17 may be configured to convert over-subscribingquestions into asynchronous responses, even if they were asked in asynchronized fashion.

The QA system 100 may include one or more QA system pipelines 100A,100B, each of which includes a computing device 104 (comprising one ormore processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) for processingquestions received over the network 140 from one or more users atcomputing devices (e.g., 110, 120, 130) connected over the network 140for communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. In this networkedarrangement, the QA system 100 and network 140 may enable QuestionAnswering (QA) generation functionality for one or more content users.Other embodiments of QA system 100 may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

In each QA system pipeline 100A, 100B, a prioritized question 19 isreceived and prioritized for processing to generate an answer 20. Insequence, prioritized questions 19 are de-queued from the sharedquestion queue 18, from which they are de-queued by the pipelineinstances for processing in priority order rather than insertion order.In selected embodiments, the question queue 18 may be implemented basedon a “priority heap” data structure. During processing within a QAsystem pipeline (e.g., 100A), questions may be split into many subtaskswhich run concurrently. A single pipeline instance can process a numberof questions concurrently, but only a certain number of subtasks. Inaddition, each QA system pipeline may include a prioritized queue (notshown) to manage the processing order of these subtasks, with thetop-level priority corresponding to the time that the correspondingquestion started (earliest has highest priority). However, it will beappreciated that such internal prioritization within each QA systempipeline may be augmented by the external target priority valuesgenerated for each question by the Question Priority Manager subsystem14 to take precedence or ranking priority over the question start time.In this way, more important or higher priority questions can “fasttrack” through the QA system pipeline if it is busy with already-runningquestions.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the question prioritization system 10, network140, a knowledge base or corpus of electronic documents 106 or otherdata, a content creator 108, content users, and other possible sourcesof input. In selected embodiments, some or all of the inputs toknowledge manager 104 may be routed through the network 140 and/or thequestion prioritization system 10. The various computing devices (e.g.,110, 120, 130, 150, 160, 170) on the network 140 may include accesspoints for content creators and content users. Some of the computingdevices may include devices for a database storing the corpus of data asthe body of information used by the knowledge manager 104 to generateanswers to cases. The network 140 may include local network connectionsand remote connections in various embodiments, such that knowledgemanager 104 may operate in environments of any size, including local andglobal, e.g., the Internet. Additionally, knowledge manager 104 servesas a front-end system that can make available a variety of knowledgeextracted from or represented in documents, network-accessible sourcesand/or structured data sources. In this manner, some processes populatethe knowledge manager with the knowledge manager also including inputinterfaces to receive knowledge requests and respond accordingly.

In one embodiment, the content creator creates content in a document 106for use as part of a corpus of data with knowledge manager 104. Thedocument 106 may include any file, text, article, or source of data(e.g., scholarly articles, encyclopedia references, textbooks, blogs,online courses of study and the like) for use in knowledge manager 104.Content users may access knowledge manager 104 via a network connectionor an Internet connection to the network 140, and may input questions toknowledge manager 104 that may be answered by the content in the corpusof data. As further described below, when a process evaluates a givensection of a document for semantic content, the process can use avariety of conventions to query it from the knowledge manager. Oneconvention is to send a well-formed question. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using Natural Language (NL)Processing. In one embodiment, the process sends well-formed questions(e.g., natural language questions, etc.) to the knowledge manager.Knowledge manager 104 may interpret the question and provide a responseto the content user containing one or more answers to the question. Insome embodiments, knowledge manager 104 may provide a response to usersin a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input prioritized question 19 and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis, e.g., comparisons, and generates a score.For example, some reasoning algorithms may look at the matching of termsand synonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 20 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information processing systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation processing systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information processing systems can benetworked together using computer network 140. Types of computer network140 that can be used to interconnect the various information processingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information processing systems. Many of theinformation processing systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the informationprocessing systems may use separate nonvolatile data stores (e.g.,server 160 utilizes nonvolatile data store 165, and mainframe computer170 utilizes nonvolatile data store 175). The nonvolatile data store canbe a component that is external to the various information processingsystems or can be internal to one of the information processing systems.An illustrative example of an information processing system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates an information processing system 202, moreparticularly, a processor and common components, which is a simplifiedexample of a computer system capable of performing the computingoperations described herein. Information processing system 202 includesa processor unit 204 that is coupled to a system bus 206. A videoadapter 208, which controls a display 210, is also coupled to system bus206. System bus 206 is coupled via a bus bridge 212 to an Input/Output(I/O) bus 214. An I/O interface 216 is coupled to I/O bus 214. The I/Ointerface 216 affords communication with various I/O devices, includinga keyboard 218, a mouse 220, a Compact Disk-Read Only Memory (CD-ROM)drive 222, a floppy disk drive 224, and a flash drive memory 226. Theformat of the ports connected to I/O interface 216 may be any known tothose skilled in the art of computer architecture, including but notlimited to Universal Serial Bus (USB) ports.

The information processing system 202 is able to communicate with aservice provider server 252 via a network 228 using a network interface230, which is coupled to system bus 206. Network 228 may be an externalnetwork such as the Internet, or an internal network such as an EthernetNetwork or a Virtual Private Network (VPN). Using network 228, clientcomputer 202 is able to use the present invention to access serviceprovider server 252.

A hard drive interface 232 is also coupled to system bus 206. Hard driveinterface 232 interfaces with a hard drive 234. In a preferredembodiment, hard drive 234 populates a system memory 236, which is alsocoupled to system bus 206. Data that populates system memory 236includes the information processing system's 202 operating system (OS)238 and software programs 244.

OS 238 includes a shell 240 for providing transparent user access toresources such as software programs 244. Generally, shell 240 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 240 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 240 (as it is called in UNIX®), also called a commandprocessor in Windows®, is generally the highest level of the operatingsystem software hierarchy and serves as a command interpreter. The shellprovides a system prompt, interprets commands entered by keyboard,mouse, or other user input media, and sends the interpreted command(s)to the appropriate lower levels of the operating system (e.g., a kernel242) for processing. While shell 240 generally is a text-based,line-oriented user interface, the present invention can also supportother user interface modes, such as graphical, voice, gestural, etc.

As depicted, OS 238 also includes kernel 242, which includes lowerlevels of functionality for OS 238, including essential servicesrequired by other parts of OS 238 and software programs 244, includingmemory management, process and task management, disk management, andmouse and keyboard management. Software programs 244 may include abrowser 246 and email client 248. Browser 246 includes program modulesand instructions enabling a World Wide Web (WWW) client (i.e.,information processing system 202) to send and receive network messagesto the Internet using HyperText Transfer Protocol (HTTP) messaging, thusenabling communication with service provider server 252. In variousembodiments, software programs 244 may also include a complex taskanalysis system 250. In these and other embodiments, the complex taskanalysis system 250 includes code for implementing the processesdescribed hereinbelow. In one embodiment, information processing system202 is able to download the complex task analysis system 250 from aservice provider server 252.

The complex task analysis system 250 performs a complex task analysisoperation during which each sentence or complete phrase in the naturallanguage text is analyzed for verbs that correlate to actions inmatching code snippets. The statements are analyzed for variables andvalues that would be inputs or outputs to a particular code snippet.Both the natural language based inputs and the previous snippet outputsare then used to determine follow up snippets that may be applicable forthe next step of execution. All code snippet sets are executed in orderof the natural language statement requests until a final outcome isachieved.

In some embodiments when a code snippet requires an additional parameterthat is not specified as an output of the previous code snippet, adefault instance of that parameter is generated that matches thesignature. For example, a file is given a generic file name and defaultconfigured location if a file name and/or configuration location is notspecified.

The hardware elements depicted in the information processing system 202are not intended to be exhaustive, but rather are representative tohighlight components used by the present invention. For instance, theinformation processing system 202 may include alternate memory storagedevices such as magnetic cassettes, Digital Versatile Disks (DVDs),Bernoulli cartridges, and the like. These and other variations areintended to be within the spirit, scope and intent of the presentinvention.

Referring to FIG. 3, a block diagram of a complex task analysis system300 is shown. In various embodiments, the complex task analysisoperation may be performed as a hardware operation, a softwareoperation, or a combination thereof. In certain embodiments, the complextask analysis system 300 includes some or all of the functions performedby the complex task analysis system 250.

The complex task analysis system 300 links a set of executable codesnippets that are deducted from natural language, to perform a morecomplicated task. The set of code snippets are analyzed based on apredetermined goal. The complex task analysis system 300 includes astatement decomposer 310, a code repository index 312, a code correlator314 and a code execution agent 316.

The statement decomposer 310 decomposes an NLP statement 318 into aplurality of statement components. In various embodiments, the statementcomponents can be identified based upon a plurality of operationsincluding a subject-verb-object (SVO) operation, a term identificationoperation, input/output identification operation, an actionidentification operation and a goal identification operation.

The code repository index 312 includes a plurality of code snippets 320,where each code snippet 320 includes an associated keyword index,machine learning (ML) identified and type (T). The associated keywordindex, ML identified and T for each code snippet 320 is influenced bymachine learning. The machine learning identified explores theconstruction and study of the algorithms contained within the codesnippet. In certain embodiments, the machine learning indication isbased upon a logistic regression operation which determines which of theset of subject verb objects or set of verbs that fall into a particularcategory of programming pattern. In various embodiments, the typeprovides an indication of a primary type of objects or classes that areidentified in the respective code snippet. For example, the type mayinclude an integer type, a person type or an account type.

The code correlator 316 correlates a statement component with a codesnippet 320. In various embodiments, the code correlator 320 matchesparameters, matches methods, determines an execution order anddetermines an execution environment.

The code execution agent 316 executes a plurality of code snippets basedupon information provided by the code correlator 320 to perform acomplex task. More specifically, the code execution agent 316 provides afirst set of inputs 340 to a first code snippet (snippet #1) 342 andgenerates a first output. The code execution agent 316 provides a secondset of inputs 350 to a second code snippet (snippet #2) 352 andgenerates a second output. The code execution agent 316 provides a thirdset of inputs 360 to a third code snippet (snippet #3) 362 and generatesa third output. Some or all of the first, second and third inputs arederived from portions of the decomposed NLP statement components as wellas some or all of a previous snippet output.

For example, the complex task analysis system 300 might receive thefollowing statements: Add numbers from 1 through 10; Find the average ofthe numbers; and, Write the numbers to the file. The complex taskanalysis system 300 would then generate code to perform the complex taskcorresponding to these statements. The complex task analysis systemanalyzes the content such as text utilizing natural language processing(NLP) to identify programmable tasks, identify code matching theprogrammable tasks and execute the code.

In various embodiments, the statement decomposer 310 forms a series ofoperational descriptions D1, D2, . . . Dn, from the statements. For eachoperational descriptor Di there is an input Ii (e.g., the statementsegment), an output Oi, and an operational description Di mapping theinput Ii to the output Oi.

The complex task analysis system 300 performs a code similarityexpansion operation using the code repository index 314. The codesimilarity expansion operation searches the repository for a codesegment (Ci) (i.e., a code snippet) that represents the requirements foreach Di to form an executable Ei. The code correlator 316 converts theseries of Di to the series of Ei; aligns the code segments and variablesand converts the output Oi to subsequent snippet input Ii of executableEi. The code execution agent 316 executes the executables Ei (i.e., thecode snippets) in sequential order (E1, E2, . . . En) to implement theseries of operational descriptions in original statements.

FIG. 4 is a generalized depiction of a complex task analysis operation400 implemented in accordance with an embodiment of the invention.

During the complex task analysis operation 400 each sentence or completephrase in the natural language text is analyzed for actions, or verbs,that correlate to matching code snippets. The statements are alsoanalyzed for variables and values that would be inputs to a particularcode snippet. Both the natural language based inputs and the codesnippet outputs are then used to determine follow up code snippets thatmay be applicable for the next step of execution. All code snippet setsare executed in order of the natural language statement requests until afinal outcome is achieved.

For example, a natural language process (e.g., a statement decompositionprocess) is applied to the sentence “I would like to add numbers from 1through 10.” The natural language process can include performing asubject, verb, object analysis on the statement. The operationaldescriptions generated by the statement decomposition process cancomprise some or all of a noun operational descriptor 420, a verboperational descriptor 422, a subject operational descriptor 424, anobject operational descriptor 426 and a proposition operationaldescriptor 428.

Next, a code similarity expansion operation 430 is performed on thedecomposed statements. Continuing the example using the examplesentence, the code similarity expansion operation 430 identifies thatcertain portions of the decomposed statements correspond a programmingpattern 440. In various embodiments, the programming pattern includes aloop operation, a sum operation and/or an addition operator operation.Machine learned patterns are applied to the verb to determine which typeof operation to apply. The code similarity expansion operation 430identifies that other portions of the decomposed statement correspond toan object 442 (e.g., is an input or an output). In various embodiments,the object includes e.g. an integer object, a number object and/or areal object.

The code similarity expansion operation 430 identifies that otherportions of the decomposed statement correspond to a particular classand variables 444. In various embodiments, the class and variableincludes e.g. a number class and/or an integer class.

The code similarity expansion operation 430 identifies that otherportions of the decomposed statement correspond to a particular methodand variables 446. In various embodiments, the method and variableincludes e.g. an add method, a sum method, an addition method and/or anaddNumbers method.

Skilled practitioners of the art will recognize that many suchembodiments are possible and the foregoing is not intended to limit thespirit, scope or intent of the invention. Specifically, many otherprogramming patterns, objects, classes and variables and methods andvariables are contemplated.

Referring to FIG. 5, a generalized flowchart of the complex taskanalysis operation 500 is shown. More specifically, the complex taskanalysis operation 500 begins operation at step 510 by decomposingnatural language statements. In various embodiments, decomposing naturallanguage statements includes parsing NLP statements into terms, parts ofspeech and tokens. Next at step 520, the complex task analysis operation500 searches a code repository using terms, types, verbs, etc. that wereidentified when decomposing the natural language statements.

Next at step 530, the complex task analysis operation 500 determinesinput types and output types from the decomposed natural languagestatements, and matches the input types and output types to codesnippets. In certain embodiments, the matching includes matching datatypes to input types for code snippets, matching outcome types to outputtypes or snippet statements, matching data types to a particularprogramming language of a code snippet and determining parameters toassociate with the code snippet. Additionally, in certain embodiments,the matching includes locating similar statements that describe the codesnippets and matching decomposed statement terms to terms and phrases insome syntactic position. Additionally, in certain embodiments, thematching includes verifying that objects and subjects conform to a datatype of a particular method or operation. Additionally, in certainembodiments, the matching includes denoting sequences and lists of datatypes from natural language statements and mapping them to parameters.

Next at step 540, the complex task analysis operation 500 orders thesequence of the code snippets based on statement order and input andoutput type matching. Next, at step 550, the complex task analysisoperation creates an execution environment for the code snippets andexecutes the ordered code snippets with the identified input parametersand retrieves a final output from the sequence of ordered code snippets.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

1. A computer-implemented method for linking a set of executable codesnippets to perform a complicated task, comprising: decomposing anatural language statement into a plurality of decomposed naturallanguage components, the natural language statement relating to acomplicated task, the complicated task comprising a plurality ofsub-tasks, the decomposing comprising parsing the natural languagestatement into terms and parts of speech; searching a repository of codesnippets to identify code snippets corresponding to each of thedecomposed natural language components; and, ordering execution of thecode snippets based upon the plurality of decomposed natural languagecomponents.
 2. The method of claim 1, further comprising: executing thecode snippets in order of the natural language statement requests untila final outcome is achieved; analyzing the plurality of decomposednatural language components for variables and values; and, associatingany identified variables and values with a particular code snippet basedupon the analysis.
 3. The method of claim 2, wherein: the associatingincludes matching data types to input types for code snippets, matchingoutcome types to output types, matching data types to a particularprogramming language of a code snippet and determining parameters toassociate with the code snippet.
 4. The method of claim 1, wherein: thedecomposed natural language statement components are identified basedupon a subject-verb-object (SVO) operation, a term identificationoperation, input/output identification operation, an actionidentification operation and a goal identification operation.
 5. Themethod of claim 1, wherein: natural language statement based inputs andcode snippet outputs are used to determine follow up snippets that areapplicable for a next step of execution.
 6. The method of claim 1,wherein: when a code snippet requires an additional parameter that isnot specified within the natural language statement, a default instanceof the additional parameter is generated that matches a signature withinthe repository of code snippets. 7-20. (canceled)